def random_game_generator() -> tuple:
"""Generate random valid board obtained by some moves from initial board.
the maximim number of moves in the board generated is 20
Returns
-------
tuple
a random game
first element is a random valid board.
second element is turn's number.
third element is draw counter.
"""
current_board = initial_board_generator()
num_of_turns = random.randint(0, 10)
draw_counter = 0
colour = {1: 'white', 0: 'black'}
for turn in range(1, num_of_turns):
# number of disks before the move:
size_before = current_board.get_number_of_disks(None)
boards = []
Moves.get_all_next_boards(current_board, colour[turn % 2], boards)
# get the next board
current_board = random.choice(boards)
# number of disks before the move:
size_after = current_board.get_number_of_disks(None)
draw_counter = update_draw_counter(draw_counter, size_before,
size_after)
return current_board, num_of_turns, draw_counter
def choose_board(self, board: Board, turn: int,
draw_counter: int) -> Board:
"""Choose the best board by applying the best move on the given board.
Parameters
----------
board : Board
the current board.
turn : int
turn's number.
draw_counter : int
counter of non-attack moves.
Returns
-------
Board
the new board, which is the result of applying
the best valid move on the given board.
"""
# generate all possible boards
boards = []
Moves.get_all_next_boards(board, self._colour, boards)
# get the fitness of every board
values = self.get_fitness(boards, turn + 1, draw_counter)
# get the id of the best board
# because our evaluation function is predict how good
# a board is for white, then when we play as 'black'
# we must takes the minimum to put the 'white' in the worst
# possible scenario.
if self._colour == 'black':
i = np.argmin(values)
else:
i = np.argmax(values)
return boards[i]
def _max(self, board: Board, depth: int, turn: int, draw_counter: int,
alpha: float, beta: float) -> float:
self._tot_num += 1 # increase the number of explored nodes.
# apply dynamic path feature if it's set to true.
# please note that this feature is under tests.
if self._dynamic_depth is True:
if self._tot_num > MiniMaxAlphaBetaSystem.maximum_nodes_number:
return np.sum(
self._pred_system.predict([board], turn, draw_counter))
# get the status if current node.
status = self._terminal_state(board, turn, draw_counter)
if status is not None:
# lose for black is win for white and vice versa.
sign = 1 if turn % 2 == 1 else -1
if status == 'win':
return 100 * sign
elif status == 'lose':
return -100 * sign
else:
return 0
# if we reach the maximum depth then stop and return
# a prediction of the current board state.
if depth >= self._depth:
return np.sum(
self._pred_system.predict([board], turn, draw_counter))
# generate the next boards by applying the valid moves.
boards = []
Moves.get_all_next_boards(board, 'white', boards)
current_max = -1e7
size_before = board.get_number_of_disks(None)
for b in boards:
next_draw_counter = draw_counter
size_after = b.get_number_of_disks(None)
next_draw_counter = update_draw_counter(next_draw_counter,
size_before, size_after)
current_max = max(
current_max,
self._min(b, depth + 1, turn + 1, next_draw_counter, alpha,
beta))
if current_max >= beta:
return current_max
alpha = max(alpha, current_max)
return current_max
def generate_random_training_set() -> tuple:
colour = {1: 'white', 0: 'black'}
current_board = initial_board_generator()
boards = [] # list of white board positions through the game.
# 2-tuple
# first element is the status (i.e 'win', 'lose', or draw).
# second element is the colour of the player with the final status.
final_status = (None, None)
start_turn = 1
draw_counter = 0
for turn in range(start_turn, 10000):
# store the boards where its white turn.
if colour[turn % 2] == 'white':
boards.append(current_board)
# get status of the game, 'None' indicates that game is still going
status = current_board.get_status(colour[turn % 2], draw_counter)
# if the game ends set the right values to final_status
# then exit the game
if status is not None:
final_status = (status, colour[turn % 2])
break
# number of disks before the move:
size_before = current_board.get_number_of_disks(None)
# get the next board
# this done by letting the agent choose the move.
next_boards = []
Moves.get_all_next_boards(current_board, colour[turn % 2],
next_boards)
current_board = random.choice(next_boards)
# number of disks before the move:
size_after = current_board.get_number_of_disks(None)
draw_counter = update_draw_counter(draw_counter, size_before,
size_after)
# get the final status for white
if final_status[1] == 'black' and final_status[0] != 'draw':
final_status = ('win' if final_status[0] == 'lose' else 'lose',
final_status[1])
return boards, final_status[0]
def _min(self, board: Board, depth: int, turn: int, draw_counter: int,
alpha: float, beta: float) -> float:
self._tot_num += 1
if self._dynamic_depth is True:
if self._tot_num > MiniMaxAlphaBetaSystem.maximum_nodes_number:
return np.sum(
self._pred_system.predict([board], turn, draw_counter))
status = self._terminal_state(board, turn, draw_counter)
if status is not None:
sign = 1 if turn % 2 == 1 else -1
if status == 'win':
return 100 * sign
elif status == 'lose':
return -100 * sign
else:
return 0
if depth >= self._depth:
return np.sum(
self._pred_system.predict([board], turn, draw_counter))
boards = []
Moves.get_all_next_boards(board, 'black', boards)
current_min = 1e7
size_before = board.get_number_of_disks(None)
for b in boards:
next_draw_counter = draw_counter
size_after = b.get_number_of_disks(None)
next_draw_counter = update_draw_counter(next_draw_counter,
size_before, size_after)
current_min = min(
current_min,
self._max(b, depth + 1, turn + 1, next_draw_counter, alpha,
beta))
if current_min <= alpha:
return current_min
beta = min(beta, current_min)
return current_min
def _f6_number_of_pieces_threatened_by_black(self, board: Board) -> float:
threatened = dict()
boards = []
Moves.get_all_next_boards(board, 'black', boards, threatened)
return len(threatened.keys())
def train(agent: Agent,
num_of_games: int,
initial_game: tuple = None,
explore_probability: float = 10,
output: bool = False,
results_output: bool = True,
plots: bool = True) -> tuple:
"""Train the agent by making it plays games against its self.
Parameters
----------
agent : Agent
The agent to train.
num_of_games : int
number of training games.
initial_game : tuple
a starting game
first element is an initial board.
second element is turn's number.
third element is draw counter.
the default is None.
explore_probability : int
the probability that the agent choose to explore a random
move to explore instead of playing the optimal move he learnt so far.
if it's None then no exploration will happened.
the probability will be 1/explore_probability.
the default is 0.1.
output: bool, optional
indicates if you want to print the game or not.
the default is False.
results_output: bool, optional
indicates if you want to print the game's results or not.
the default is True.
plots: bool, optional
indicates if you want to print the plots for errors
in prediction or not.
the default is True.
Returns
-------
tuple
first element, list:
i-th element is the error after updating parameters using i-th game
second element, list:
i-th element is the result for on the i-th game.
"""
costs = []
cli = CLI() # object to use the console interface if output is True.
# dictionary for convert status into numbers.
d = {'win': 1, 'lose': -1, 'draw': 0}
# dictionary for knowing the colour of the current player
# 1 is the first player
# 0 is the second player
colour = {1: 'white', 0: 'black'}
# used to restore the agent colour after training end
# thats because we are updating evaluation based on white position.
previous_colour = agent.get_colour()
results = [] # store the results of the games.
# the probability that the agent will choose random move.
prob = explore_probability
data_set = {'board': [], 'f_status': []}
for i in range(num_of_games):
boards = [] # list of white board positions through the game.
if initial_game is None:
# generate the initial board
current_board = initial_board_generator()
start_turn = 1
draw_counter = 0
else:
current_board, start_turn, draw_counter = initial_game
# 2-tuple
# first element is the status (i.e 'win', 'lose', or draw).
# second element is the colour of the player with the final status.
final_status = (None, None)
for turn in range(start_turn, 10000):
# alternate the roles of the same agent by setting its colour
# to the colour of the player who should move in the current turn
agent.set_colour(colour[turn % 2])
# store the boards when its white turn.
if colour[turn % 2] == 'white':
boards.append(current_board)
# get status of the game, 'None' indicates that game is still going
status = current_board.get_status(colour[turn % 2], draw_counter)
# print the turn number and the board if we need them.
if output is True:
print(f'turn: {turn}')
cli.show(current_board)
# if the game ends set the right values to final_status
# then exit the game
if status is not None:
final_status = (status, colour[turn % 2])
break
# number of disks before the move:
size_before = current_board.get_number_of_disks(None)
# get the next board
# this done by letting the agent choose the move.
if prob is not None:
r = random.randint(1, prob)
if prob is not None and r == 1:
next_boards = []
Moves.get_all_next_boards(current_board, colour[turn % 2],
next_boards)
current_board = random.choice(next_boards)
else:
current_board = agent.choose_board(current_board, turn,
draw_counter)
# number of disks before the move:
size_after = current_board.get_number_of_disks(None)
draw_counter = update_draw_counter(draw_counter, size_before,
size_after)
# get the final status for white
if final_status[1] == 'black' and final_status[0] != 'draw':
final_status = ('win' if final_status[0] == 'lose' else 'lose',
final_status[1])
# add the cost before updating.
costs.append(agent.get_system().compute_error(boards, final_status[0]))
# let the agent learn using the boards positions through the game.
# agent.learn(boards, final_status[0])
# add the result of the game for white player
results.append(d[final_status[0]])
data_set['board'].append(boards)
data_set['f_status'].append(final_status[0])
if (i + 1) % 100 == 0:
print(f'{i+1} games finished unitl now!')
# reset the colour of the agent
agent.set_colour(previous_colour)
# let the agent learn from the previous games
m = min(results.count(-1), results.count(1))
lose_m = m
win_m = m
for i in range(num_of_games):
bs = data_set['board'][i]
fs = data_set['f_status'][i]
if fs == 'win' and win_m > 0:
agent.learn(bs, fs)
win_m -= 1
elif fs == 'lose' and lose_m > 0:
agent.learn(bs, fs)
lose_m -= 1
elif fs == 'draw':
agent.learn(bs, fs)
if results_output is True:
wins = results.count(1)
loses = results.count(-1)
draws = results.count(0)
print(f'training results of agent {agent.get_name()}')
print(f'wins: {wins}')
print(f'loses: {loses}')
print(f'draws: {draws}')
print('##################################')
print(f'cost: {costs[-1]}')
if plots is True:
print_iter = num_of_games // num_of_games
plt.plot(np.arange(0, num_of_games, print_iter + 1),
costs[::print_iter + 1], 'k')
plt.show()
return costs, results